/*=========================================================================
 *
 *  Copyright NumFOCUS
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *         https://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  Unless required by applicable law or agreed to in writing, software
 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/
#ifndef itkDiffusionTensor3DReconstructionImageFilter_hxx
#define itkDiffusionTensor3DReconstructionImageFilter_hxx

#include "itkMath.h"
#include "itkImageRegionConstIteratorWithIndex.h"
#include "itkImageRegionIterator.h"
#include "itkArray.h"
#include "itkImageMaskSpatialObject.h"
#include "vnl/vnl_vector.h"
#include "itkTotalProgressReporter.h"

namespace itk
{
template <typename TReferenceImagePixelType,
          typename TGradientImagePixelType,
          typename TTensorPixelType,
          typename TMaskImageType>
DiffusionTensor3DReconstructionImageFilter<TReferenceImagePixelType,
                                           TGradientImagePixelType,
                                           TTensorPixelType,
                                           TMaskImageType>::DiffusionTensor3DReconstructionImageFilter()
{
  // At least 1 inputs is necessary for a vector image.
  // For images added one at a time we need at least six
  this->SetNumberOfRequiredInputs(1);
  m_NumberOfGradientDirections = 0;
  m_NumberOfBaselineImages = 1;
  m_Threshold = NumericTraits<ReferencePixelType>::min();
  m_GradientImageTypeEnumeration = DiffusionTensor3DReconstructionImageFilterEnums::GradientImageFormat::Else;
  m_GradientDirectionContainer = nullptr;
  m_TensorBasis.set_identity();
  m_BValue = 1.0;
  m_MaskImagePresent = false;
  this->DynamicMultiThreadingOn();
  this->ThreaderUpdateProgressOff();
}

template <typename TReferenceImagePixelType,
          typename TGradientImagePixelType,
          typename TTensorPixelType,
          typename TMaskImageType>
void
DiffusionTensor3DReconstructionImageFilter<TReferenceImagePixelType,
                                           TGradientImagePixelType,
                                           TTensorPixelType,
                                           TMaskImageType>::BeforeThreadedGenerateData()
{
  // If we have more than 3 inputs, then each input, except the first is a
  // gradient image. The number of gradient images must match the number of
  // gradient directions.
  const ProcessObject::DataObjectPointerArraySizeType numberOfInputs = this->GetNumberOfIndexedInputs();

  // If there is only 1 gradient image, it must be an itk::VectorImage.
  // Otherwise
  // we must have a container of (numberOfInputs-1) itk::Image. Check to make
  // sure
  if ((numberOfInputs == 1 || (numberOfInputs == 2 && this->m_MaskImagePresent)) &&
      m_GradientImageTypeEnumeration !=
        DiffusionTensor3DReconstructionImageFilterEnums::GradientImageFormat::GradientIsInASingleImage)
  {
    std::string gradientImageClassName(this->ProcessObject::GetInput(0)->GetNameOfClass());
    if (strcmp(gradientImageClassName.c_str(), "VectorImage") != 0)
    {
      itkExceptionMacro("There is only one Gradient image. I expect that to be a VectorImage. "
                        << "But its of type: " << gradientImageClassName);
    }
  }

  this->ComputeTensorBasis();

  // If there's a mask, make sure it matches the dimensions of the
  // gradient image(s).
  if (!this->m_MaskImagePresent)
  {
    return;
  }
  typename ImageMaskSpatialObject<3>::Pointer maskSpatialObject =
    dynamic_cast<ImageMaskSpatialObject<3> *>(this->ProcessObject::GetInput(1));
  if (maskSpatialObject.IsNull())
  {
    return; // not a mask image
  }
  typename MaskImageType::ConstPointer maskImage = maskSpatialObject->GetImage();

  typename MaskImageType::SizeType maskSize = maskImage->GetLargestPossibleRegion().GetSize();
  typename MaskImageType::SizeType refSize;

  typename MaskImageType::PointType maskOrigin = maskImage->GetOrigin();
  typename MaskImageType::PointType refOrigin;

  typename MaskImageType::SpacingType maskSpacing = maskImage->GetSpacing();
  typename MaskImageType::SpacingType refSpacing;

  typename MaskImageType::DirectionType maskDirection = maskImage->GetDirection();
  typename MaskImageType::DirectionType refDirection;

  if (m_GradientImageTypeEnumeration ==
      DiffusionTensor3DReconstructionImageFilterEnums::GradientImageFormat::GradientIsInManyImages)
  {
    auto * refImage = static_cast<ReferenceImageType *>(this->ProcessObject::GetInput(0));
    refSize = refImage->GetLargestPossibleRegion().GetSize();
    refOrigin = refImage->GetOrigin();
    refSpacing = refImage->GetSpacing();
    refDirection = refImage->GetDirection();
  }
  else if (m_GradientImageTypeEnumeration ==
           DiffusionTensor3DReconstructionImageFilterEnums::GradientImageFormat::GradientIsInASingleImage)
  {
    auto * gradientImage = static_cast<GradientImagesType *>(this->ProcessObject::GetInput(0));
    refSize = gradientImage->GetLargestPossibleRegion().GetSize();
    refOrigin = gradientImage->GetOrigin();
    refSpacing = gradientImage->GetSpacing();
    refDirection = gradientImage->GetDirection();
  }
  // size mismatch is a deal breaker. Iterators are useless.
  if (refSize != maskSize)
  {
    itkExceptionMacro("Mask size doesn't match Reference Image Size"
                      << " Mask Size " << maskSize << " Ref Size " << refSize);
  }
  // Origin, Spacing, Direction, should match but it isn't fatal if
  // they don't.
  if (refOrigin != maskOrigin)
  {
    itkWarningMacro("Mask origin doesn't match Reference origin "
                    << "Mask Origin " << maskOrigin << " Ref Origin " << refOrigin);
  }
  if (refSpacing != maskSpacing)
  {
    itkWarningMacro("Mask spacing doesn't match Reference spacing "
                    << "Mask Spacing " << maskSpacing << " Ref Spacing " << refSpacing);
  }
  if (refDirection != maskDirection)
  {
    itkWarningMacro("Mask direction doesn't match Reference direction "
                    << "Mask Direction " << maskDirection << " Ref Direction " << refDirection);
  }
}

// POTENTIAL WARNING:
//
// Until we fix netlib svd routines, we will need to set the number of thread
// to 1.
template <typename TReferenceImagePixelType,
          typename TGradientImagePixelType,
          typename TTensorPixelType,
          typename TMaskImageType>
void
DiffusionTensor3DReconstructionImageFilter<TReferenceImagePixelType,
                                           TGradientImagePixelType,
                                           TTensorPixelType,
                                           TMaskImageType>::DynamicThreadedGenerateData(const OutputImageRegionType &
                                                                                          outputRegionForThread)
{
  typename OutputImageType::Pointer outputImage = static_cast<OutputImageType *>(this->ProcessObject::GetOutput(0));

  ImageRegionIterator<OutputImageType> oit(outputImage, outputRegionForThread);

  vnl_vector<double> B(m_NumberOfGradientDirections);
  vnl_vector<double> D(6);

  // if a mask is present, iterate through mask image and skip zero voxels
  typename MaskSpatialObjectType::Pointer maskSpatialObject;
  if (this->m_MaskImagePresent)
  {
    maskSpatialObject = static_cast<MaskSpatialObjectType *>(this->ProcessObject::GetInput(1));
  }
  bool useMask(maskSpatialObject.IsNotNull());

  // Two cases here .
  // 1. If the Gradients have been specified in multiple images, we will create
  // 'n' iterators for each of the gradient images and solve the Stejskal-Tanner
  // equations for every pixel.
  // 2. If the Gradients have been specified in a single multi-component image,
  // one iterator will suffice to do the same.

  TotalProgressReporter progress(this, outputImage->GetRequestedRegion().GetNumberOfPixels());

  if (m_GradientImageTypeEnumeration ==
      DiffusionTensor3DReconstructionImageFilterEnums::GradientImageFormat::GradientIsInManyImages)
  {
    typename ReferenceImageType::Pointer refImage = static_cast<ReferenceImageType *>(this->ProcessObject::GetInput(0));
    ImageRegionConstIteratorWithIndex<ReferenceImageType> it(refImage, outputRegionForThread);
    it.GoToBegin();

    using GradientIteratorType = ImageRegionConstIterator<GradientImageType>;
    std::vector<GradientIteratorType *> gradientItContainer;

    for (unsigned int i = 1; i <= m_NumberOfGradientDirections; ++i)
    {

      typename GradientImageType::Pointer gradientImagePointer =
        dynamic_cast<GradientImageType *>(this->ProcessObject::GetInput(i + 1));
      if (gradientImagePointer.IsNull())
      {
        itkExceptionMacro("Invalid dynamic_cast");
      }

      auto * git = new GradientIteratorType(gradientImagePointer, outputRegionForThread);
      git->GoToBegin();
      gradientItContainer.push_back(git);
    }

    // Iterate over the reference and gradient images and solve the Stejskal
    // equations to reconstruct the Diffusion tensor.
    // See splweb.bwh.harvard.edu:8000/pages/papers/westin/ISMRM2002.pdf
    // "A Dual Tensor Basis Solution to the Stejskal-Tanner Equations for
    // DT-MRI"

    while (!it.IsAtEnd())
    {

      ReferencePixelType b0 = it.Get();

      TensorPixelType tensor(0.0);

      //
      // if a mask is present, and we don't have a zero pixel
      // look up the voxel in the mask image corresponding to
      // the location of the current index.
      bool unmaskedPixel(true);
      if (useMask)
      {
        typename ImageRegionConstIteratorWithIndex<ReferenceImageType>::IndexType index = it.GetIndex();
        typename ReferenceImageType::PointType                                    point;
        refImage->TransformIndexToPhysicalPoint(index, point);
        unmaskedPixel = maskSpatialObject->IsInsideInWorldSpace(point);
      }

      if (Math::NotAlmostEquals(b0, itk::NumericTraits<ReferencePixelType>::ZeroValue()) && unmaskedPixel &&
          (b0 >= m_Threshold))
      {
        for (unsigned int i = 0; i < m_NumberOfGradientDirections; ++i)
        {
          GradientPixelType b = gradientItContainer[i]->Get();

          if (Math::AlmostEquals(b, itk::NumericTraits<GradientPixelType>::ZeroValue()))
          {
            B[i] = 0;
          }
          else
          {
            B[i] = -std::log(static_cast<double>(b) / static_cast<double>(b0)) / this->m_BValue;
          }

          ++(*gradientItContainer[i]);
        }

        vnl_svd<double> pseudoInverseSolver{ m_TensorBasis.as_matrix() };
        if (m_NumberOfGradientDirections > 6)
        {
          D = pseudoInverseSolver.solve(m_BMatrix * B);
        }
        else
        {
          D = pseudoInverseSolver.solve(B);
        }

        tensor(0, 0) = D[0];
        tensor(0, 1) = D[1];
        tensor(0, 2) = D[2];
        tensor(1, 1) = D[3];
        tensor(1, 2) = D[4];
        tensor(2, 2) = D[5];
      }
      else
      {
        for (unsigned int i = 0; i < m_NumberOfGradientDirections; ++i)
        {
          ++(*gradientItContainer[i]);
        }
      }

      oit.Set(tensor);
      ++oit;
      ++it;
      progress.CompletedPixel();
    }

    for (unsigned int i = 0; i < gradientItContainer.size(); ++i)
    {
      delete gradientItContainer[i];
    }
  }
  // The gradients are specified in a single multi-component image
  else if (m_GradientImageTypeEnumeration ==
           DiffusionTensor3DReconstructionImageFilterEnums::GradientImageFormat::GradientIsInASingleImage)
  {
    using GradientIteratorType = ImageRegionConstIteratorWithIndex<GradientImagesType>;
    using GradientVectorType = typename GradientImagesType::PixelType;
    typename GradientImagesType::Pointer gradientImagePointer = nullptr;

    // Would have liked a dynamic_cast here, but seems SGI doesn't like it
    // The enum will ensure that an inappropriate cast is not done
    gradientImagePointer = itkDynamicCastInDebugMode<GradientImagesType *>(this->ProcessObject::GetInput(0));

    GradientIteratorType git(gradientImagePointer, outputRegionForThread);
    git.GoToBegin();

    // Compute the indices of the baseline images and gradient images
    std::vector<unsigned int> baselineind; // contains the indices of
                                           // the baseline images
    std::vector<unsigned int> gradientind; // contains the indices of
                                           // the gradient images

    for (GradientDirectionContainerType::ConstIterator gdcit = this->m_GradientDirectionContainer->Begin();
         gdcit != this->m_GradientDirectionContainer->End();
         ++gdcit)
    {
      if (gdcit.Value().one_norm() <= 0.0)
      {
        baselineind.push_back(gdcit.Index());
      }
      else
      {
        gradientind.push_back(gdcit.Index());
      }
    }

    while (!git.IsAtEnd())
    {
      GradientVectorType b = git.Get();

      typename NumericTraits<ReferencePixelType>::AccumulateType b0 = NumericTraits<ReferencePixelType>::ZeroValue();

      // Average the baseline image pixels
      for (const auto & i : baselineind)
      {
        b0 += b[i];
      }
      b0 /= this->m_NumberOfBaselineImages;

      TensorPixelType tensor(0.0);

      //
      // if a mask is present, and we don't have a zero pixel
      // look up the voxel in the mask image corresponding to
      // the location of the current index.
      bool unmaskedPixel(true);
      if (useMask)
      {
        typename ImageRegionConstIteratorWithIndex<ReferenceImageType>::IndexType index = git.GetIndex();
        typename ReferenceImageType::PointType                                    point;

        gradientImagePointer->TransformIndexToPhysicalPoint(index, point);
        unmaskedPixel = maskSpatialObject->IsInsideInWorldSpace(point);
      }

      if (Math::NotAlmostEquals(b0, NumericTraits<ReferencePixelType>::ZeroValue()) && unmaskedPixel &&
          (b0 >= m_Threshold))
      {
        for (unsigned int i = 0; i < m_NumberOfGradientDirections; ++i)
        {
          if (Math::AlmostEquals(b[gradientind[i]], NumericTraits<typename GradientVectorType::ValueType>::ZeroValue()))
          {
            B[i] = 0;
          }
          else
          {
            B[i] = -std::log(static_cast<double>(b[gradientind[i]]) / static_cast<double>(b0)) / this->m_BValue;
          }
        }

        vnl_svd<double> pseudoInverseSolver{ m_TensorBasis.as_matrix() };
        if (m_NumberOfGradientDirections > 6)
        {
          D = pseudoInverseSolver.solve(m_BMatrix * B);
        }
        else
        {
          D = pseudoInverseSolver.solve(B);
        }

        tensor(0, 0) = D[0];
        tensor(0, 1) = D[1];
        tensor(0, 2) = D[2];
        tensor(1, 1) = D[3];
        tensor(1, 2) = D[4];
        tensor(2, 2) = D[5];
      }

      oit.Set(tensor);
      ++oit; // Output (reconstructed tensor image) iterator
      ++git; // Gradient  image iterator
      progress.CompletedPixel();
    }
  }
}

template <typename TReferenceImagePixelType,
          typename TGradientImagePixelType,
          typename TTensorPixelType,
          typename TMaskImageType>
void
DiffusionTensor3DReconstructionImageFilter<TReferenceImagePixelType,
                                           TGradientImagePixelType,
                                           TTensorPixelType,
                                           TMaskImageType>::ComputeTensorBasis()
{
  // This is only important if we are using a vector image.  For
  // images added one at a time, this is not needed but doesn't hurt.
  std::vector<unsigned int> gradientind;
  for (GradientDirectionContainerType::ConstIterator gdcit = this->m_GradientDirectionContainer->Begin();
       gdcit != this->m_GradientDirectionContainer->End();
       ++gdcit)
  {
    if (gdcit.Value().one_norm() > 0.0)
    {
      gradientind.push_back(gdcit.Index());
    }
  }

  m_BMatrix.set_size(m_NumberOfGradientDirections, 6);
  for (unsigned int m = 0; m < m_NumberOfGradientDirections; ++m)
  {
    m_BMatrix[m][0] = m_GradientDirectionContainer->ElementAt(gradientind[m])[0] *
                      m_GradientDirectionContainer->ElementAt(gradientind[m])[0];
    m_BMatrix[m][1] = 2 * m_GradientDirectionContainer->ElementAt(gradientind[m])[0] *
                      m_GradientDirectionContainer->ElementAt(gradientind[m])[1];
    m_BMatrix[m][2] = 2 * m_GradientDirectionContainer->ElementAt(gradientind[m])[0] *
                      m_GradientDirectionContainer->ElementAt(gradientind[m])[2];
    m_BMatrix[m][3] = m_GradientDirectionContainer->ElementAt(gradientind[m])[1] *
                      m_GradientDirectionContainer->ElementAt(gradientind[m])[1];
    m_BMatrix[m][4] = 2 * m_GradientDirectionContainer->ElementAt(gradientind[m])[1] *
                      m_GradientDirectionContainer->ElementAt(gradientind[m])[2];
    m_BMatrix[m][5] = m_GradientDirectionContainer->ElementAt(gradientind[m])[2] *
                      m_GradientDirectionContainer->ElementAt(gradientind[m])[2];
  }

  if (m_NumberOfGradientDirections > 6)
  {
    m_TensorBasis = m_BMatrix.transpose() * m_BMatrix;
  }
  else
  {
    m_TensorBasis = m_BMatrix;
  }

  m_BMatrix.inplace_transpose();
}

template <typename TReferenceImagePixelType,
          typename TGradientImagePixelType,
          typename TTensorPixelType,
          typename TMaskImageType>
const Image<TGradientImagePixelType, 3> *
DiffusionTensor3DReconstructionImageFilter<TReferenceImagePixelType,
                                           TGradientImagePixelType,
                                           TTensorPixelType,
                                           TMaskImageType>::GetGradientImage(unsigned int index) const
{
  if (m_GradientImageTypeEnumeration ==
      DiffusionTensor3DReconstructionImageFilterEnums::GradientImageFormat::GradientIsInASingleImage)
  {
    itkExceptionMacro("Cannot retrieve individual gradient Image if "
                      << "all gradients are in a single image.");
  }
  // input 0 is either the single gradient image, or the reference
  // image. input 1 is either null or a mask image.
  return itkDynamicCastInDebugMode<const GradientImageType *>(this->ProcessObject::GetInput(index + 2));
}

template <typename TReferenceImagePixelType,
          typename TGradientImagePixelType,
          typename TTensorPixelType,
          typename TMaskImageType>
void
DiffusionTensor3DReconstructionImageFilter<TReferenceImagePixelType,
                                           TGradientImagePixelType,
                                           TTensorPixelType,
                                           TMaskImageType>::AddGradientImage(const GradientDirectionType &
                                                                                                       gradientDirection,
                                                                             const GradientImageType * gradientImage)
{
  // Make sure crazy users did not call both AddGradientImage and
  // SetGradientImage
  if (m_GradientImageTypeEnumeration ==
      DiffusionTensor3DReconstructionImageFilterEnums::GradientImageFormat::GradientIsInASingleImage)
  {
    itkExceptionMacro("Cannot call both methods:"
                      << "AddGradientImage and SetGradientImage. Please call only one of them.");
  }

  // If the container to hold the gradient directions hasn't been allocated
  // yet, allocate it.
  if (!this->m_GradientDirectionContainer)
  {
    this->m_GradientDirectionContainer = GradientDirectionContainerType::New();
  }

  m_GradientDirectionContainer->InsertElement(m_NumberOfGradientDirections,
                                              gradientDirection / gradientDirection.two_norm());
  ++m_NumberOfGradientDirections;
  this->ProcessObject::SetNthInput(m_NumberOfGradientDirections + 1, const_cast<GradientImageType *>(gradientImage));
  m_GradientImageTypeEnumeration =
    DiffusionTensor3DReconstructionImageFilterEnums::GradientImageFormat::GradientIsInManyImages;
}

template <typename TReferenceImagePixelType,
          typename TGradientImagePixelType,
          typename TTensorPixelType,
          typename TMaskImageType>
void
DiffusionTensor3DReconstructionImageFilter<TReferenceImagePixelType,
                                           TGradientImagePixelType,
                                           TTensorPixelType,
                                           TMaskImageType>::SetGradientImage(GradientDirectionContainerType *
                                                                                                        gradientDirection,
                                                                             const GradientImagesType * gradientImage)
{
  // Make sure crazy users did not call both AddGradientImage and
  // SetGradientImage
  if (m_GradientImageTypeEnumeration ==
      DiffusionTensor3DReconstructionImageFilterEnums::GradientImageFormat::GradientIsInManyImages)
  {
    itkExceptionMacro("Cannot call both methods:"
                      << "AddGradientImage and SetGradientImage. Please call only one of them.");
  }

  this->m_GradientDirectionContainer = gradientDirection;

  unsigned int numImages = gradientDirection->Size();
  this->m_NumberOfBaselineImages = 0;
  for (GradientDirectionContainerType::Iterator it = this->m_GradientDirectionContainer->Begin();
       it != this->m_GradientDirectionContainer->End();
       it++)
  {
    if (it.Value().one_norm() <= 0.0)
    {
      this->m_NumberOfBaselineImages++;
    }
    else // Normalize non-zero gradient directions
    {
      it.Value() = it.Value() / it.Value().two_norm();
    }
  }

  this->m_NumberOfGradientDirections = numImages - this->m_NumberOfBaselineImages;

  // ensure that the gradient image we received has as many components as
  // the number of gradient directions
  if (gradientImage->GetVectorLength() != this->m_NumberOfBaselineImages + this->m_NumberOfGradientDirections)
  {
    itkExceptionMacro(<< this->m_NumberOfGradientDirections << " gradients + " << this->m_NumberOfBaselineImages
                      << "baselines = " << this->m_NumberOfGradientDirections + this->m_NumberOfBaselineImages
                      << " directions specified but image has " << gradientImage->GetVectorLength() << " components.");
  }

  this->ProcessObject::SetNthInput(0, const_cast<GradientImagesType *>(gradientImage));
  m_GradientImageTypeEnumeration =
    DiffusionTensor3DReconstructionImageFilterEnums::GradientImageFormat::GradientIsInASingleImage;
}

template <typename TReferenceImagePixelType,
          typename TGradientImagePixelType,
          typename TTensorPixelType,
          typename TMaskImageType>
void
DiffusionTensor3DReconstructionImageFilter<TReferenceImagePixelType,
                                           TGradientImagePixelType,
                                           TTensorPixelType,
                                           TMaskImageType>::SetMaskSpatialObject(MaskSpatialObjectType *
                                                                                   maskSpatialObject)
{
  this->ProcessObject::SetNthInput(1, maskSpatialObject);
  this->m_MaskImagePresent = true;
}

template <typename TReferenceImagePixelType,
          typename TGradientImagePixelType,
          typename TTensorPixelType,
          typename TMaskImageType>
void
DiffusionTensor3DReconstructionImageFilter<TReferenceImagePixelType,
                                           TGradientImagePixelType,
                                           TTensorPixelType,
                                           TMaskImageType>::SetMaskImage(MaskImageType * maskImage)
{
  typename ImageMaskSpatialObject<3>::Pointer maskSpatialObject = ImageMaskSpatialObject<3>::New();
  maskSpatialObject->SetImage(maskImage);
  this->SetMaskSpatialObject(maskSpatialObject);
}

template <typename TReferenceImagePixelType,
          typename TGradientImagePixelType,
          typename TTensorPixelType,
          typename TMaskImageType>
void
DiffusionTensor3DReconstructionImageFilter<TReferenceImagePixelType,
                                           TGradientImagePixelType,
                                           TTensorPixelType,
                                           TMaskImageType>::PrintSelf(std::ostream & os, Indent indent) const
{
  Superclass::PrintSelf(os, indent);

  os << indent << "TensorBasisMatrix: " << m_TensorBasis << std::endl;
  os << indent << "Coeffs: " << m_BMatrix << std::endl;
  if (m_GradientDirectionContainer)
  {
    os << indent << "GradientDirectionContainer: " << m_GradientDirectionContainer << std::endl;
  }
  else
  {
    os << indent << "GradientDirectionContainer: (Gradient directions not set)" << std::endl;
  }
  os << indent << "NumberOfGradientDirections: " << m_NumberOfGradientDirections << std::endl;
  os << indent << "NumberOfBaselineImages: " << m_NumberOfBaselineImages << std::endl;
  os << indent << "Threshold for reference B0 image: " << m_Threshold << std::endl;
  os << indent << "BValue: " << m_BValue << std::endl;
  if (this->m_GradientImageTypeEnumeration ==
      DiffusionTensor3DReconstructionImageFilterEnums::GradientImageFormat::GradientIsInManyImages)
  {
    os << indent << "Gradient images haven been supplied " << std::endl;
  }
  else if (this->m_GradientImageTypeEnumeration ==
           DiffusionTensor3DReconstructionImageFilterEnums::GradientImageFormat::GradientIsInManyImages)
  {
    os << indent << "A multicomponent gradient image has been supplied" << std::endl;
  }
}

template <typename TReferenceImagePixelType,
          typename TGradientImagePixelType,
          typename TTensorPixelType,
          typename TMaskImageType>
void
DiffusionTensor3DReconstructionImageFilter<TReferenceImagePixelType,
                                           TGradientImagePixelType,
                                           TTensorPixelType,
                                           TMaskImageType>::VerifyPreconditions() ITKv5_CONST
{
  Superclass::VerifyPreconditions();

  if (this->m_NumberOfBaselineImages == 0)
  {
    itkExceptionMacro("Number of baseline images is null");
  }
  if (this->m_NumberOfGradientDirections < 6)
  {
    itkExceptionMacro("Not enough gradient directions supplied. Need to supply at least 6");
  }
}

} // namespace itk

#endif
